In this study, we constructed a mental stress recognition model with Electrocardiogram (ECG) signals by machine learning. Firstly, we collected the ECG signals while students are explaining simple math exercises, located the R-wave peak of the QRS wave group, and calculated the R-wave to R-wave (RR) interval. Then we extracted the characteristic parameters of autonomic nervous response from the RR interval, carried out statistical analysis and sequential forward selection and finally constructed a psychological stress recognition model. The accuracy of the model in the test set and the independent subject validation set was 79% and 83%, respectively. The results show that it is feasible to recognize strong or weak psychological stress state through machine learning method.
KEYWORDS: Heart, Education and training, Feature extraction, Electrocardiography, Data modeling, Data analysis, Reflection, Nervous system, Material fatigue, Fractal analysis
This article explored the effect of physical activity time and that of the heart rate during physical activity on mental fatigue intervention. First, we acquired electrocardiogram from 104 subjects, calculated the inter-beat intervals (IBI), removed the abnormal values in the IBI series, segmented and divided the IBI series into training and validation datasets and application datasets according to the inclusion criteria of mental fatigued and non-fatigued states. Second, we extracted 39 linear and non-linear RR parameters as fatigue physiological features, and applied Leave-One-Subject-Out cross test and Sequential Backward Selection algorithm for feature selection while training some traditional classifiers. Third, we applied the best trained classifier to detect mental fatigue before and after physical activity. Finally, we analyzed the change of mental fatigue status before and after physical activity and obtained the following three findings: (1) physical activity did not have intervention effect on mental fatigue for 62% observed cases; (2) for 38% observed cases, physical activity could aggravate or relieve mental fatigue; (3) for the observed cases that the physical activity time was in the range of 5-15 minutes, the average heart rate in 100-140 beat per minute during physical activity was more likely to relieve mental fatigue
Stress refers to a series of physiological reactions to help the human body overcome threats. In this study, the machine learning method was used to analyze the stress in students' daily life environment. The purpose is to explore the neurophysiological model of sleep under stress, so that they can effectively adjust their personal stress and improve their sleep quality. First, we collected ECG and triple-axis acceleration data of 33 college students in their daily life, calculated the inter-beat intervals from every two consecutive R waves in these data. Secondly, we extracted 39 linear and non-linear RR parameters as stress physiological features, and applied Leave-One-Subject-Out cross test and Sequential Backward Selection algorithm for feature selection while training some traditional classifiers. Finally, a binary classification model of stressed and non-stressed states recognition in the first hour of sleep at night was constructed, and the generalization accuracy of the model was 75.00% on the validation data set independent of model training and feature selection. The results show that it is feasible to monitor students' stress state at night by machine learning.
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